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Record W3007302706 · doi:10.1002/edm2.119

Assessment of lipid response to acute olanzapine administration in healthy adults

2020· article· en· W3007302706 on OpenAlexafffund
Avital Nahmias, Priska Stahel, Satya Dash

Bibliographic record

VenueEndocrinology Diabetes & Metabolism · 2020
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsDiabetes CanadaUniversity of TorontoUniversity Health Network
FundersDiabetes Canada
KeywordsOlanzapineMedicineType 2 diabetesInternal medicineDiabetes mellitusWeight gainEndocrinologyAdministration (probate law)Body weightAntipsychoticPsychiatrySchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

Abstract Background Atypical antipsychotics (AAP) can induce hypertriglyceridaemia and type 2 diabetes. Weight gain contributes to these effects, but there is evidence that AAP can have acute metabolic effects on glycaemia independent of weight change. Aims We undertook a single‐blind crossover study in eight healthy volunteers to assess whether the AAP olanzapine acutely increases triglyceride and free fatty acid in response to a high‐fat oral load (50 g fat with no carbohydrate) and whether these effects are attenuated by the dopamine D2 receptor agonist bromocriptine. Methods Participants underwent three treatments in random order: Olanzapine 10 mg plus placebo (OL + PL), Olanzapine 10 mg plus bromocriptine 5 mg (OL + BR) and placebo plus placebo (PL + PL). Results Olanzapine increased plasma prolactin, an effect that was reversed by co‐administration of the D2 receptor agonist bromocriptine ( P = .0002). There were no significant differences in postprandial triglyceride ( P = .8), free fatty acid ( P = .4) or glucose ( P = .8). Conclusion These results suggest that AAPs likely do not directly increase postprandial lipids but may do so indirectly via changes in body weight and/or glycaemia.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.499
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.020
GPT teacher head0.339
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2020
Admission routes2
Has abstractyes

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